--- datasets: - multimolecule/rnacentral library_name: multimolecule license: agpl-3.0 mask_token: pipeline_tag: fill-mask tags: - Biology - RNA - ncRNA - rna widget: - example_title: microRNA 21 mask_index: 15 mask_index_1based: 16 masked_char: A output: - label: AUU score: 0.036454 - label: GAU score: 0.034317 - label: CUU score: 0.0274 - label: AAG score: 0.024828 - label: UUG score: 0.02376 pipeline_tag: fill-mask sequence_type: ncRNA task: fill-mask text: UAGCUUAUCAGACUGUUG - example_title: microRNA 146a mask_index: 15 mask_index_1based: 16 masked_char: A output: - label: AAU score: 0.059322 - label: AAC score: 0.039853 - label: GAU score: 0.038141 - label: AAA score: 0.036878 - label: GGA score: 0.025595 pipeline_tag: fill-mask sequence_type: ncRNA task: fill-mask text: UGAGAACUGAAUUCCGGU - example_title: microRNA 155 mask_index: 15 mask_index_1based: 16 masked_char: A output: - label: UGU score: 0.030609 - label: AUU score: 0.02823 - label: UUU score: 0.027554 - label: AAU score: 0.027152 - label: GAA score: 0.02533 pipeline_tag: fill-mask sequence_type: ncRNA task: fill-mask text: UUAAUGCUAAUCGUGGGGGUU - example_title: RNA component of mitochondrial RNA processing endoribonuclease mask_index: 12 mask_index_1based: 13 masked_char: A output: - label: UUC score: 0.030126 - label: CUC score: 0.027686 - label: UUU score: 0.027425 - label: UCU score: 0.024937 - label: CUG score: 0.024189 pipeline_tag: fill-mask sequence_type: ncRNA task: fill-mask text: GGUUCGUGCUGACCUGUAUCCUAGGCUACACACUGAGGACUCUGUUCCUCCCCUUUCCGCCUAGGGGAAAGUCCCCGGACCUCGGGCAGAGAGUGCCACGUGCAUACGCACGUAGACAUUCCCCGCUUCCCACUCCAAAGUCCGCCAAGAAGCGUAUCCCGCUGAGCGGCGUGGCGCGGGGGCGUCAUCCGUCAGCUCCCUCUAGUUACGCAGGCAGUGCGUGUCCGCGCACCAACCACACGGGGCUCAUUCUCAGCGCGGCUGUAAAAAAAA - example_title: 7SK small nuclear RNA mask_index: 24 mask_index_1based: 25 masked_char: A output: - label: GGC score: 0.052339 - label: GCC score: 0.049248 - label: CCC score: 0.040614 - label: UCC score: 0.037157 - label: GAG score: 0.031449 pipeline_tag: fill-mask sequence_type: ncRNA task: fill-mask text: GGAUGUGAGGGCGAUCUGGCUGCGUCUGUCACCCCAUUGAUCGCCAGGGUUGAUUCGGCUGAUCUGGCUGGCUAGGCGGGUGUCCCCUUCCUCCCUCACCGCUCCAUGUGCGUCCCUCCCGAAGCUGCGCGCUCGGUCGAAGAGGACGACCAUCCCCGAUAGAGGAGGACCGGUCUUCGGUCAAGGGUAUACGAGUAGCUGCGCUCCCCUGCUAGAACCUCCAAACAAGCUCUCAAGGUCCAUUUGUAGGAGAACGUAGGGUAGUCAAGCUUCCAAGACUCCAGACACAUCCAAAUGAGGCGCUGCAUGUGGCAGUCUGCCUUUCUU - example_title: telomerase RNA component mask_index: 36 mask_index_1based: 37 masked_char: A output: - label: UGG score: 0.082349 - label: GCC score: 0.065632 - label: UGC score: 0.034984 - label: GUG score: 0.030916 - label: GCG score: 0.027791 pipeline_tag: fill-mask sequence_type: ncRNA task: fill-mask text: GGGUUGCGGAGGGUGGGCCUGGGAGGGGUGGUGGCCUUUUGUCUAACCCUAACUGAGAAGGGCGUAGGCGCCGUGCUUUUGCUCCCCGCGCGCUGUUUUUCUCGCUGACUUUCAGCGGGCGGAAAAGCCUCGGCCUGCCGCCUUCCACCGUUCAUUCUAGAGCAAACAAAAAAUGUCAGCUGCUGGCCCGUUCGCCCCUCCCGGGGACCUGCGGCGGGUCGCCUGCCCAGCCCCCGAACCCCGCCUGGAGGCCGCGGUCGGCCCGGGGCUUCUCCGGAGGCACCCACUGCCACCGCGAAGAGUUGGGCUCUGUCAGCCGCGGGUCUCUCGGGGGCGAGGGCGAGGUUCAGGCCUUUCAGGCCGCAGGAAGAGGAACGGAGCGAGUCCCCGCGCGCGGCGCGAUUCCCUGAGCUGUGGGACGUGCACCCAGGACUCGGCUCACACAUG - example_title: vault RNA 2-1 mask_index: 12 mask_index_1based: 13 masked_char: A output: - label: UUC score: 0.036486 - label: GGC score: 0.036387 - label: UCC score: 0.033614 - label: GGA score: 0.032683 - label: UGG score: 0.02959 pipeline_tag: fill-mask sequence_type: ncRNA task: fill-mask text: CGGGUCGGAGUUUCAAGCGGUUACCUCCUCAUGCCGGACUUUCUAUCUGUCCAUCUCUGUGCUGGGGUUCGAGACCCGCGGGUGCUUACUGACCCUUUUAUGCAA - example_title: brain cytoplasmic RNA 1 mask_index: 18 mask_index_1based: 19 masked_char: A output: - label: GGG score: 0.058228 - label: CGG score: 0.029714 - label: GCC score: 0.025698 - label: GCU score: 0.023683 - label: UGG score: 0.023109 pipeline_tag: fill-mask sequence_type: ncRNA task: fill-mask text: GGCCGGGCGCGGUGGCUCCCUGUAAUCCCAGCUCUCAGGGAGGCUAAGAGGCGGGAGGAUAGCUUGAGCCCAGGAGUUCGAGACCUGCCUGGGCAAUAUAGCGAGACCCCGUUCUCCAGAAAAAGGAAAAAAAAAAACAAAAGACAAAAAAAAAAUAAGCGUAACUUCCCUCAAAGCAACAACCCCCCCCCCCCU - example_title: HIV-1 TAR-WT mask_index: 15 mask_index_1based: 16 masked_char: A output: - label: UGG score: 0.055933 - label: UCC score: 0.039563 - label: GAG score: 0.034423 - label: GGA score: 0.027744 - label: AUG score: 0.024329 pipeline_tag: fill-mask sequence_type: ncRNA task: fill-mask text: GGUCUCUCUGGUUAGAGAUCUGAGCCUGGGAGCUCUCUGGCUAACUAGGGAACC - example_title: prion protein (Kanno blood group) mask_index: 21 mask_index_1based: 22 masked_char: A output: - label: CUG score: 0.083667 - label: UUC score: 0.058037 - label: GUG score: 0.042056 - label: UUU score: 0.039099 - label: UAU score: 0.032602 pipeline_tag: fill-mask sequence_type: mRNA task: fill-mask text: AUGGCGAACCUUGGCUGCUGGCUGGUUCUCUUUGUGGCCACAUGGAGUGACCUGGGCCUCUGC - example_title: interleukin 10 mask_index: 39 mask_index_1based: 40 masked_char: A output: - label: CUG score: 0.178429 - label: AUG score: 0.133451 - label: CUC score: 0.077188 - label: AGC score: 0.061544 - label: GCC score: 0.056949 pipeline_tag: fill-mask sequence_type: mRNA task: fill-mask text: AUGCACAGCUCAGCACUGCUCUGUUGCCUGGUCCUCCUGGGGGUGAGGGCC - example_title: Zaire ebolavirus mask_index: 45 mask_index_1based: 46 masked_char: A output: - label: GAA score: 0.044874 - label: GAU score: 0.039572 - label: UUU score: 0.035055 - label: GUU score: 0.033566 - label: AUU score: 0.028965 pipeline_tag: fill-mask sequence_type: mRNA task: fill-mask text: AAUGUUCAAACACUUUGUGAAGCUCUGUUAGCUGAUGGUCUUGCUGCAUUUCCUAGCAAUAUGAUGGUAGUCACAGAGCGUGAGCAAAAAGAAAGCUUAUUGCAUCAAGCAUCAUGGCACCACACAAGUGAUGAUUUUGGUGAGCAUGCCACAGUUAGAGGGAGUAGCUUUGUAACUGAUUUAGAGAAAUACAAUCUUGCAUUUAGAUAUGAGUUUACAGCACCUUUUAUAGAAUAUUGUAACCGUUGCUAUGGUGUUAAGAAUGUUUUUAAUUGGAUGCAUUAUACAAUCCCACAGUGUUAU - example_title: SARS coronavirus mask_index: 24 mask_index_1based: 25 masked_char: A output: - label: UUU score: 0.153463 - label: UUG score: 0.086199 - label: CUU score: 0.064142 - label: UUA score: 0.058406 - label: UUC score: 0.049124 pipeline_tag: fill-mask sequence_type: mRNA task: fill-mask text: AUGUUUAUUUUCUUAUUAUUUCUUCUCACUAGUGGUAGUGACCUUGACCGGUGCACCACUUUUGAUGAUGUUCAAGCUCCUAAUUACACUCAACAUACUUCAUCUAUGAGGGGGGUUUACUAUCCUGAUGAAAUUUUUAGAUCAGACACUCUUUAUUUAACUCAGGAUUUAUUUCUUCCAUUUUAUUCUAAUGUUACAGGGUUUCAUACUAUUAAUCAUACGUUUGACAACCCUGUCAUACCUUUUAAGGAUGGUAUUUAUUUUGCUGCCACAGAGAAAUCAAAUGUUGUCCGUGGUUGGGUUUUUGGUUCUACCAUGAACAACAAGUCACAGUCGGUGAUUAUUAUUAACAAUUCUACUAAUGUUGUUAUACGAGCAUGUAACUUUGAAUUGUGUGACAACCCUUUCUUUGCUGUUUCUAAACCCAUGGGUACACAGACACAUACUAUGAUAUUCGAUAAUGCAUUUAAAUGCACUUUCGAGUACAUAUCU - example_title: insulin mask_index: 12 mask_index_1based: 13 masked_char: A output: - label: AUG score: 0.999765 - label: AUC score: 0.000225 - label: CCU score: 3.0e-06 - label: ACA score: 2.0e-06 - label: GUG score: 1.0e-06 pipeline_tag: fill-mask sequence_type: mRNA task: fill-mask text: AUGGCCCUGUGGCGCCUCCUGCCCCUGCUGGCGCUGCUGGCCCUCUGGGGACCUGACCCAGCCGCAGCCUUUGUGAACCAACACCUGUGCGGCUCACACCUGGUGGAAGCUCUCUACCUAGUGUGCGGGGAACGAGGCUUCUUCUACACACCCAAGACCCGCCGGGAGGCAGAGGACCUGCAGGUGGGGCAGGUGGAGCUGGGCGGGGGCCCUGGUGCAGGCAGCCUGCAGCCCUUGGCCCUGGAGGGGUCCCUGCAGAAGCGUGGCAUUGUGGAACAAUGCUGUACCAGCAUCUGCUCCCUCUACCAGCUGGAGAACUACUGCAACUAG - example_title: cyclin dependent kinase inhibitor 2A mask_index: 18 mask_index_1based: 19 masked_char: A output: - label: GGC score: 0.313019 - label: GCG score: 0.094953 - label: GAC score: 0.050381 - label: CUG score: 0.04516 - label: GGA score: 0.044861 pipeline_tag: fill-mask sequence_type: mRNA task: fill-mask text: AUGGAGCCGGCGGCGGGGAGCAUGGAGCCUUCGGCUGACUGGCUGGCCACGGCCGCGGCCCGGGGUCGGGUAGAGGAGGUGCGGGCGCUGCUGGAGGCGGGGGCGCUGCCCAACGCACCGAAUAGUUACGGUCGGAGGCCGAUCCAGGUCAUGAUGAUGGGCAGCGCCCGAGUGGCGGAGCUGCUGCUGCUCCACGGCGCGGAGCCCAACUGCGCCGACCCCGCCACUCUCACCCGACCCGUGCACGACGCUGCCCGGGAGGGCUUCCUGGACACGCUGGUGGUGCUGCACCGGGCCGGGGCGCGGCUGGACGUGCGCGAUGCCUGGGGCCGUCUGCCCGUGGACCUGGCUGAGGAGCUGGGCCAUCGCGAUGUCGCACGGUACCUGCGCGCGGCUGCGGGGGGCACCAGAGGCAGUAACCAUGCCCGCAUAGAUGCCGCGGAAGGUCCCUCAGACAUCCCCGAUUGA - example_title: human papillomavirus type 16 E6 mask_index: 12 mask_index_1based: 13 masked_char: A output: - label: AAA score: 0.042913 - label: CCA score: 0.039063 - label: AAG score: 0.034515 - label: GAA score: 0.030832 - label: AGA score: 0.024598 pipeline_tag: fill-mask sequence_type: mRNA task: fill-mask text: AUGCACCAAAAGACUGCAAUGUUUCAGGACCCACAGGAGCGACCCAGAAAGUUACCACAGUUAUGCACAGAGCUGCAAACAACUAUACAUGAUAUAAUAUUAGAAUGUGUGUACUGCAAGCAACAGUUACUGCGACGUGAGGUAUAUGACUUUGCUUUUCGGGAUUUAUGCAUAGUAUAUAGAGAUGGGAAUCCAUAUGCUGUAUGUGAUAAAUGUUUAAAGUUUUAUUCUAAAAUUAGUGAGUAUAGACAUUAUUGUUAUAGUUUGUAUGGAACAACAUUAGAACAGCAAUACAACAAACCGUUGUGUGAUUUGUUAAUUAGGUGUAUUAACUGUCAAAAGCCACUGUGUCCUGAAGAAAAGCAAAGACAUCUGGACAAAAAGCAAAGAUUCCAUAAUAUAAGGGGUCGGUGGACCGGUCGAUGUAUGUCUUGUUGCAGAUCAUCAAGAACACGUAGAGAAACCCAGCUGUAA - example_title: NRAS proto-oncogene mask_index: 36 mask_index_1based: 37 masked_char: A output: - label: CGG score: 0.038451 - label: CCG score: 0.037105 - label: UCG score: 0.036003 - label: AUG score: 0.034392 - label: ACU score: 0.032722 pipeline_tag: fill-mask sequence_type: 5' UTR task: fill-mask text: GGGGCCGGAAGUGCCGCUCCUUGGUGGGGGCUGUUCGCGGUUCCGGGGUCUCCAACAUUUUUCCCGGCUGUGGUCCUAAAUCUGUCCAAAGCAGAGGCAGUGGAGCUUGAGGUUCUUGCUGGUGUG - example_title: amyloid beta precursor protein mask_index: 15 mask_index_1based: 16 masked_char: A output: - label: GGC score: 0.163655 - label: GGA score: 0.075258 - label: GGU score: 0.061764 - label: CGC score: 0.044964 - label: GAC score: 0.033283 pipeline_tag: fill-mask sequence_type: 5' UTR task: fill-mask text: GUCAGUUUCCUCGGCGGUAGGCGAGAGCACGCGGAGGAGCGUGCGCGGGGGCCCCGGGAGACGGCGGCGGUGGCGGCGCGGGCAGAGCAAGGACGCGGCGGAUCCCACUCGCACAGCAGCGCACUCGGUGCCCCGCGCAGGGUCGCG - example_title: RUNX family transcription factor 1 mask_index: 15 mask_index_1based: 16 masked_char: A output: - label: AGA score: 0.044242 - label: AAA score: 0.042336 - label: AUG score: 0.039409 - label: AAG score: 0.036489 - label: UGG score: 0.032637 pipeline_tag: fill-mask sequence_type: 5' UTR task: fill-mask text: ACUUCUUUGGGCCUCAACAACCACAGAACCACAAGUUGGGUAGCCUGGCAGUGUCAGAAGUCUGAACCCAGCAUAGUGGUCAGCAGGCAGGACGAAUCACACUGAAUGCAAACCACAGGGUUUCGCAGCGUGGUAAAAGAAAUCAUUGAGUCCCCCGCCUUCAGAAGAGGGUGCAUUUUCAGGAGGAAG - example_title: fragile X messenger ribonucleoprotein 1 mask_index: 15 mask_index_1based: 16 masked_char: A output: - label: GCG score: 0.104931 - label: GGC score: 0.090209 - label: GGG score: 0.040038 - label: AGC score: 0.037217 - label: GUC score: 0.031719 pipeline_tag: fill-mask sequence_type: 5' UTR task: fill-mask text: CUCAGUCAGGCGCUCUCCGUUUCGGUUUCACUUCCGGUGGAGGGCCGCCUCUGAGCGGGCGGCGGGCCGACGGCGAGCGCGGGCGGCGGCGGUGACGGAGGCGCCGCUGCCAGGGGGCGUGCGGCAGCGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGAGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCUGGGCCUCGAGCGCCCGCAGCCCACCUCUCGGGGGCGGGCUCCCGGCGCUAGCAGGGCUGAAGAGAAG - example_title: MYC proto-oncogene mask_index: 39 mask_index_1based: 40 masked_char: A output: - label: GCC score: 0.080427 - label: GCG score: 0.072732 - label: GGC score: 0.069955 - label: GAG score: 0.051612 - label: UGG score: 0.041913 pipeline_tag: fill-mask sequence_type: 5' UTR task: fill-mask text: AACUCGCUGUAGUAAUUCCAGCGAGAGGCAGAGGGAGCGGGGCGGCCGGCUAGGGUGGAAGAGCCGGGCGAGCAGAGCUGCGCUGCGGGCGUCCUGGGAAGGGAGAUCCGGAGCGAAUAGGGGGCUUCGCCUCUGGCCCAGCCCUCCCGCUGAUCCCCCAGCCAGCGGUCCGCAACCCUUGCCGCAUCCACGAAACUUUGCCCAUAGCAGCGGGCGGGCACUUUGCACUGGAACUUACAACACCCGAGCAAGGACGCGACUCUCCCGACGCGGGGAGGCUAUUCUGCCCAUUUGGGGACACUUCCCCGCCGCUGCCAGGACCCGCUUCUCUGAAAGGCUCUCCUUGCAGCUGCUUAGACG - example_title: activating transcription factor 4 mask_index: 24 mask_index_1based: 25 masked_char: A output: - label: CCC score: 0.054324 - label: CCG score: 0.039298 - label: CUC score: 0.034159 - label: CCU score: 0.030659 - label: CCA score: 0.0273 pipeline_tag: fill-mask sequence_type: 5' UTR task: fill-mask text: CAUUUCUACUUUGCCCGCCCACAGUAGUUUUCUCUGCGCGUGUGCGUUUUCCCUCCUCCCCGCCCUCAGGGUCCACGGCCACCAUGGCGUAUUAGGGGCAGCAGUGCCUGCGGCAGCAUUGGCCUUUGCAGCGGCGGCAGCAGCACCAGGCUCUGCAGCGGCAACCCCCAGCGGCUUAAGCCAUGGCGCUUCUCACGGCAUUCAGCAGCAGCGUUGCUGUAACCGACAAAGACACCUUCGAAUUAAGCACAUUCCUCGAUUCCAGCAAAGCACCGCAAC - example_title: Human GPI protein p137 mask_index: 12 mask_index_1based: 13 masked_char: A output: - label: AAA score: 0.055483 - label: AUU score: 0.045112 - label: AAU score: 0.034645 - label: UUU score: 0.032741 - label: AAG score: 0.029208 pipeline_tag: fill-mask sequence_type: 3' UTR task: fill-mask text: UUUUUAAAAGGAGAUACCAAAUGCCUGCUGCUACCACCCUUUUCAAUUGCUAUGUUUUGAAAGGCACCAGUAUGUGUUUUAGAUUGAUUUAAAUGUUUCAUUUAAAUCACGGACAGUAGUUUCAGUUCUGAUGGUAUAAGCAAAACAAAUAAAACGUUUAUAAAAGUUGUAUCUUGAAACACUGGUGUUCAACAGCUAGCAGCUUAUGUGAUUCACCCCAUGCCACGUUAGUGUCACAAAUUUUAUGGUUUAUCUCCAGCAACAUUUCUCUAGUACUUGCACUUAUUAUCUGAAUUC - example_title: nucleophosmin 1 mask_index: 12 mask_index_1based: 13 masked_char: A output: - label: AAA score: 0.047198 - label: UCA score: 0.041406 - label: AAU score: 0.032262 - label: UUU score: 0.031668 - label: AUU score: 0.031399 pipeline_tag: fill-mask sequence_type: 3' UTR task: fill-mask text: GAAAAUAGUUUAAAUUUGUUAAAAAAUUUUCCGUCUUAUUUCAUUUCUGUAACAGUUGAUAUCUGGCUGUCCUUUUUAUAAUGCAGAGUGAGAACUUUCCCUACCGUGUUUGAUAAAUGUUGUCCAGGUUCUAUUGCCAAGAAUGUGUUGUCCAAAAUGCCUGUUUAGUUUUUAAAGAUGGAACUCCACCCUUUGCUUGGUUUUAAGUAUGUAUGGAAUGUUAUGAUAGGACAUAGUAGUAGCGGUGGUCAGACAUGGAAAUGGUGGGGAGACAAAAAUAUACAUGUGAAAUAAAACUCAGUAUUUUAAUAAAGUAGCACGGUUUCUAUU - example_title: superoxide dismutase 1 mask_index: 12 mask_index_1based: 13 masked_char: A output: - label: AAG score: 0.044194 - label: AAA score: 0.032457 - label: AUU score: 0.027957 - label: AAU score: 0.023298 - label: CUC score: 0.023021 pipeline_tag: fill-mask sequence_type: 3' UTR task: fill-mask text: ACAUUCCCUUGGUAGUCUGAGGCCCCUUAACUCAUCUGUUAUCCUGCUAGCUGUAGAAAUGUAUCCUGAUAAACAUUAAACACUGUAAUCUUAAAAGUGUAAUUGUGUGACUUUUUCAGAGUUGCUUUAAAGUACCUGUAGUGAGAAACUGAUUUAUGAUCACUUGGAAGAUUUGUAUAGUUUUAUAAAACUCAGUUAAAAUGUCUGUUUCAAUGACCUGUAUUUUGCCAGACUUAAAUCACAGAUGGGUAUUAAACUUGUCAGAAUUUCUUUGUCAUUCAAGCCUGUGAAUAAAAACCCUGUAUGGCACUUAUUAUGAGGCUAUUAAAAGAAUCCAAAUUCAAACUA - example_title: hemoglobin subunit alpha 2 mask_index: 42 mask_index_1based: 43 masked_char: A output: - label: UCG score: 0.108342 - label: CCA score: 0.074268 - label: CCG score: 0.053831 - label: GCU score: 0.032195 - label: CCU score: 0.030543 pipeline_tag: fill-mask sequence_type: 3' UTR task: fill-mask text: CUGGAGCCUCGGUAGCCGUUCCUCCUGCCCGCUGGGCCUCCCGGGCCCUCCUCCCCUCCUUGCACCGGCCCUUCCUGGUCUUUGAAUAAAGUCUGAGUGGGCAGC - example_title: BRAF proto-oncogene mask_index: 12 mask_index_1based: 13 masked_char: A output: - label: AAA score: 0.05836 - label: GAA score: 0.055404 - label: AUG score: 0.05109 - label: GAG score: 0.048152 - label: UUG score: 0.041588 pipeline_tag: fill-mask sequence_type: 3' UTR task: fill-mask text: AACAAAUGAGUGGAGUUCAGGAGAGUAGCAACAAAAGGAAAAUAAAUGAACAUAUGUUUGCUUAUAUGUUAAAUUGAAUAAAAUACUCUCUUUUUUUUUAAGGUGAACCAAAGAACACUUGUGUGGUUAAAGACUAGAUAUAAUUUUUCCCCAAACUAAAAUUUAUACUUAACAUUGGAUUUUUAACAUCCAAGGGUUAAAAUACAUAGACAUUGCUAAAAAUUGGCAGAGCCUCUUCUAGAGGCUUUACUUUCUGUUCCGGGUUUGUAUCAUUCACUUGGUUAUUUUAAGUAGUAAACUUCAGUUUCUCAUGCAACUUUUGUUGCCAGCUAUCACAUGUCCACUAGGGACUCCAGAAGAAGACCCUACCUAUGCCUGUGUUUGCAGGUGAGAAGUUGGCAGUCGGUUAGCCUG - example_title: H3 clustered histone 1 mask_index: 24 mask_index_1based: 25 masked_char: A output: - label: AAA score: 0.047706 - label: UUC score: 0.040209 - label: CAA score: 0.032907 - label: AUC score: 0.027633 - label: AGA score: 0.027074 pipeline_tag: fill-mask sequence_type: 3' UTR task: fill-mask text: UUACUGUGGUCUCUCUGACGGUCCCAAAGGCUCUUUUCAGAGCCACCACCUUUU --- # RNA-FM Pre-trained model on non-coding RNA (ncRNA) using a masked language modeling (MLM) objective. ## Disclaimer This is an UNOFFICIAL implementation of the [Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions](https://doi.org/10.1101/2022.08.06.503062) by Jiayang Chen, Zhihang Hu, Siqi Sun, et al. The OFFICIAL repository of RNA-FM is at [ml4bio/RNA-FM](https://github.com/ml4bio/RNA-FM). > [!TIP] > The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation. **The team releasing RNA-FM did not write this model card for this model so this model card has been written by the MultiMolecule team.** ## Model Details RNA-FM is a [bert](https://huggingface.co/google-bert/bert-base-uncased)-style model pre-trained on a large corpus of non-coding RNA sequences in a self-supervised fashion. This means that the model was trained on the raw nucleotides of RNA sequences only, with an automatic process to generate inputs and labels from those texts. Please refer to the [Training Details](#training-details) section for more information on the training process. ### Variants - **[multimolecule/rnafm](https://huggingface.co/multimolecule/rnafm)**: The RNA-FM model pre-trained on non-coding RNA sequences. - **[multimolecule/mrnafm](https://huggingface.co/multimolecule/mrnafm)**: The RNA-FM model pre-trained on messenger RNA sequences. ### Model Specification
Variants Num Layers Hidden Size Num Heads Intermediate Size Num Parameters (M) FLOPs (G) MACs (G) Max Num Tokens
mRNA-FM 12 1280 20 5120 239.26 258.08 128.85 1024
RNA-FM 640 99.52 109.02 54.36
### Links - **Code**: [multimolecule.rnafm](https://github.com/DLS5-Omics/multimolecule/tree/master/multimolecule/models/rnafm) - **Data**: [multimolecule/rnacentral](https://huggingface.co/datasets/multimolecule/rnacentral) - **Paper**: [Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions](https://doi.org/10.1101/2022.08.06.503062) - **Developed by**: Jiayang Chen, Zhihang Hu, Siqi Sun, Qingxiong Tan, Yixuan Wang, Qinze Yu, Licheng Zong, Liang Hong, Jin Xiao, Tao Shen, Irwin King, Yu Li - **Model type**: [BERT](https://huggingface.co/google-bert/bert-base-uncased) - [ESM](https://huggingface.co/facebook/esm2_t48_15B_UR50D) - **Original Repository**: [ml4bio/RNA-FM](https://github.com/ml4bio/RNA-FM) ## Usage The model file depends on the [`multimolecule`](https://multimolecule.danling.org) library. You can install it using pip: ```bash pip install multimolecule ``` ### Direct Use #### Masked Language Modeling You can use this model directly with a pipeline for masked language modeling: ```python import multimolecule # you must import multimolecule to register models from transformers import pipeline predictor = pipeline("fill-mask", model="multimolecule/rnafm") output = predictor("gguccucugguuagaccagaucugagccu") ``` ### Downstream Use #### Extract Features Here is how to use this model to get the features of a given sequence in PyTorch: ```python from multimolecule import RnaTokenizer, RnaFmModel tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnafm") model = RnaFmModel.from_pretrained("multimolecule/rnafm") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") output = model(**input) ``` #### Sequence Classification / Regression > [!NOTE] > This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for sequence classification or regression. Here is how to use this model as backbone to fine-tune for a sequence-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, RnaFmForSequencePrediction tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnafm") model = RnaFmForSequencePrediction.from_pretrained("multimolecule/rnafm") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") label = torch.tensor([1]) output = model(**input, labels=label) ``` #### Token Classification / Regression > [!NOTE] > This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for token classification or regression. Here is how to use this model as backbone to fine-tune for a nucleotide-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, RnaFmForTokenPrediction tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnafm") model = RnaFmForTokenPrediction.from_pretrained("multimolecule/rnafm") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") label = torch.randint(2, (len(text), )) output = model(**input, labels=label) ``` #### Contact Classification / Regression > [!NOTE] > This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for contact classification or regression. Here is how to use this model as backbone to fine-tune for a contact-level task in PyTorch: ```python import torch from multimolecule import RnaTokenizer, RnaFmForContactPrediction tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnafm") model = RnaFmForContactPrediction.from_pretrained("multimolecule/rnafm") text = "UAGCUUAUCAGACUGAUGUUG" input = tokenizer(text, return_tensors="pt") label = torch.randint(2, (len(text), len(text))) output = model(**input, labels=label) ``` ## Training Details RNA-FM used Masked Language Modeling (MLM) as the pre-training objective: taking a sequence, the model randomly masks 15% of the tokens in the input then runs the entire masked sentence through the model and has to predict the masked tokens. This is comparable to the Cloze task in language modeling. ### Training Data The RNA-FM model was pre-trained on [RNAcentral](https://multimolecule.danling.org/datasets/rnacentral). RNAcentral is a free, public resource that offers integrated access to a comprehensive and up-to-date set of non-coding RNA sequences provided by a collaborating group of [Expert Databases](https://rnacentral.org/expert-databases) representing a broad range of organisms and RNA types. RNA-FM applied [CD-HIT (CD-HIT-EST)](https://sites.google.com/view/cd-hit) with a cut-off at 100% sequence identity to remove redundancy from the RNAcentral. The final dataset contains 23.7 million non-redundant RNA sequences. RNA-FM preprocessed all tokens by replacing "U"s with "T"s. Note that during model conversions, "T" is replaced with "U". [`RnaTokenizer`][multimolecule.RnaTokenizer] will convert "T"s to "U"s for you, you may disable this behaviour by passing `replace_T_with_U=False`. ### Training Procedure #### Preprocessing RNA-FM used masked language modeling (MLM) as the pre-training objective. The masking procedure is similar to the one used in BERT: - Mask rate: 15% - Replacement: `` for 80% of masked tokens - Replacement: random token for 10% of masked tokens - Replacement: unchanged token for 10% of masked tokens #### Pre-training The model was trained on 8 NVIDIA A100 GPUs with 80GiB memories. - Learning rate: 1e-4 - Learning rate scheduler: Inverse square root - Learning rate warm-up: 10,000 steps - Weight decay: 0.01 ## Citation ```bibtex @article{chen2022interpretable, title={Interpretable rna foundation model from unannotated data for highly accurate rna structure and function predictions}, author={Chen, Jiayang and Hu, Zhihang and Sun, Siqi and Tan, Qingxiong and Wang, Yixuan and Yu, Qinze and Zong, Licheng and Hong, Liang and Xiao, Jin and King, Irwin and others}, journal={arXiv preprint arXiv:2204.00300}, year={2022} } ``` > [!NOTE] > The artifacts distributed in this repository are part of the MultiMolecule project. > If MultiMolecule supports your research, please cite the MultiMolecule project as follows: ```bibtex @software{chen_2024_12638419, author = {Chen, Zhiyuan and Zhu, Sophia Y.}, title = {MultiMolecule}, doi = {10.5281/zenodo.12638419}, publisher = {Zenodo}, url = {https://doi.org/10.5281/zenodo.12638419}, year = 2024, month = may, day = 4 } ``` ## Contact Please use GitHub issues of [MultiMolecule](https://github.com/DLS5-Omics/multimolecule/issues) for any questions or comments on the model card. Please contact the authors of the [RNA-FM paper](https://doi.org/10.1101/2022.08.06.503062) for questions or comments on the paper/model. ## License This model implementation is licensed under the [GNU Affero General Public License](license.md). For additional terms and clarifications, please refer to our [License FAQ](license-faq.md). ```spdx SPDX-License-Identifier: AGPL-3.0-or-later ```